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Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In wartime, the spare parts warehouse or transport carrier may be attacked deliberately, and some storage and transportation capacity may be completely or partially lost, which leads to the interruption of spare parts supply system. The interruption risk of supply network is an uncertain parameter and difficult to be predicted and change dynamically. In view of the depth uncertainty of interruption risk, the diversification of wartime spare parts supply optimization objectives is considered. A joint optimization model of multi-objective location-allocation for wartime spare parts supply is constructed for the minimum cost of spare parts supply and the shortest lead time of supply. A corresponding robust optimization model is established based on the robust equivalent change of polyhedron uncertainty set. The meta-heuristic algorithms are used to obtain the non-dominate solution sets and the corresponding supply plans. The results show that the efficiency and reliability of spare parts supply network can be greatly improved by strengthening some key nodes. On the other hand, the optimal solution of the robust optimization model can guarantee the feasibility of the worst-case solution of spare parts supply, that is, the model has good robustness. [ABSTRACT FROM AUTHOR]